Visualization of Genomic Data
Plotting with Gviz
- Function
AnnotationTrack()creates a track object for plotting plotTracks()function is he main interface when plotting single track objects, or lists of tracks linked together across the same genomic coordinates. The resulting plots are very similar to the graphical output of the UCSC Genome Browser
chr7
"hg19" [1] "chr7"
Adding coordinates
Combining multiple tracks
gr1 <- GRanges(
seqnames = Rle(c("chr1", "chr2"), c(3, 2)),
ranges = IRanges(start = c(1, 5, 10, 15, 20), end = c(4, 8, 12, 18, 25)),
strand = Rle(strand(c("+", "-", "+", "-", "+")))
)
gr2 <- GRanges(
seqnames = Rle(c("chr1", "chr2"), c(3, 2)),
ranges = IRanges(start = c(2, 6, 11, 16, 21), end = c(5, 9, 13, 19, 26)),
strand = Rle(strand(c("+", "-", "+", "-", "+")))
)
track1 <- AnnotationTrack(gr1, name = "Track 1")
track2 <- AnnotationTrack(gr2, name = "Track 2")
plotTracks(list(gtrack, track1, track2))
Only the chr1 region is shown in the plot - if region is not specified, the entire first chromosome is plotted.
Specifying the region for plotting
Use the from and to arguments to specify the region of interest. The chromosome argument specifies the chromosome to plot.
Adding ideogram
If ideogram data is available, it can be added to the plot using the IdeogramTrack() function. The chromosome argument specifies the chromosome to plot.
Ideogram tracks are the one exception in all of Gviz’s track objects in the sense that they are not really displayed on the same coordinate system like all the other tracks. Instead, the current genomic location is indicated on the chromosome by a red box/line
chr7
"hg19" 
Adding gene models
Setting coordinates for plotting
plotTracks supports the from and to arguments that let us choose an arbitrary genomic range to plot.
Zooming to the base pair level
When zooming further we may take a look at the actual genomic sequence at a given position
Adding a quantitative track
We can add a quantitative track to the plot using the DataTrack() function. The data argument specifies the data to plot, and the type argument specifies the type of plot (e.g., “histogram”, “density”, “heatmap”).
# making example data
set.seed(123)
lim <- c(26700000, 26750000)
coords <- seq(lim[1], lim[2], length.out = 101) |>
round()
dat <- runif(100, min = -10, max = 10)
dtrack <- DataTrack(data = dat, start = coords[-length(coords)],
end = coords[-1], chromosome = chr, genome = gen,
name = "Uniform")
plotTracks(list(itrack, gtrack, atrack, grtrack, dtrack),
from = lim[1], to = lim[2])
Changing type of quantitative track
Plotting interaction data
Annotation of interaction data
Highlighting regions with high score
gi_sub <- gi[order(gi$score, decreasing = TRUE)]
gi_sub <- head(gi_sub[distance(first(gi_sub), second(gi_sub))>200000], n=5)
start(regions(gi_sub)) <- start(regions(gi_sub))-40000
end(regions(gi_sub)) <- end(regions(gi_sub))+40000
viewTracks(trackList(ctcf, heatmap, heightDist = c(1, 3)),
gr=range, autoOptimizeStyle = TRUE)
## add TAD information
addInteractionAnnotation(tads, "heatmap", grid.lines, gp=gpar(col="#E69F00", lwd=3, lty=3))
addInteractionAnnotation(gi_sub, "heatmap", grid.polygon, gp=gpar(col="red", lwd=2, lty=2, fill=NA))